Automated classification of sea-ice types in Synthetic Aperture Radar (SAR) imagery is complicated by the class-dependent decrease of backscatter intensity with Incidence Angle (IA). In the log-domain, this decrease is approximately linear over the typical range of space-borne SAR instruments. A global correction does not consider that different surface types show different rates of decrease in backscatter intensity. Here, we introduce a supervised classification algorithm that directly incorporates the surface-type dependent effect of IA. We replace the constant mean vector of a Gaussian probability density function in a Bayesian classifier with a linearly variable mean. During training, the classifier first retrieves the slope and intercept of the linear function describing the mean value and then calculates the covariance matrix as the mean squared deviation relative to this function. The IA dependence is no longer treated as an image property but as a class property. Based on training and validation data selected from overlapping SAR and optical images, we evaluate the proposed method in several case studies and compare to other classification algorithms for which a global IA correction is applied during pre-processing. Our results show that the inclusion of the per-class IA sensitivity can significantly improve the performance of the classifier.

中文翻译：

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考虑入射角的表面类型相关效应，从 Sentinel-1 映射海冰类型

合成孔径雷达 (SAR) 图像中海冰类型的自动分类由于后向散射强度随入射角 (IA) 的下降而变得复杂。在对数域中，这种下降在星载 SAR 仪器的典型范围内近似线性。全局校正不考虑不同的表面类型显示出不同的反向散射强度降低率。在这里，我们介绍了一种直接结合了 IA 的表面类型依赖效应的监督分类算法。我们将贝叶斯分类器中的高斯概率密度函数的常数均值向量替换为线性变量均值。培训期间，分类器首先检索描述平均值的线性函数的斜率和截距，然后将协方差矩阵计算为相对于该函数的均方偏差。IA 依赖不再被视为图像属性，而是作为类属性。基于从重叠 SAR 和光学图像中选择的训练和验证数据，我们在几个案例研究中评估了所提出的方法，并与在预处理期间应用全局 IA 校正的其他分类算法进行了比较。我们的结果表明，包含每类 IA 敏感性可以显着提高分类器的性能。基于从重叠 SAR 和光学图像中选择的训练和验证数据，我们在几个案例研究中评估了所提出的方法，并与在预处理期间应用全局 IA 校正的其他分类算法进行了比较。我们的结果表明，包含每类 IA 敏感性可以显着提高分类器的性能。基于从重叠 SAR 和光学图像中选择的训练和验证数据，我们在几个案例研究中评估了所提出的方法，并与在预处理期间应用全局 IA 校正的其他分类算法进行了比较。我们的结果表明，包含每类 IA 敏感性可以显着提高分类器的性能。